Choosing the Correct Statistical Test in SAS, Stata, SPSS and R

The following table shows general guidelines for choosing a statistical
analysis. We emphasize that these are general guidelines and should not be
construed as hard and fast rules. Usually your data could be analyzed in
multiple ways, each of which could yield legitimate answers. The table below
covers a number of common analyses and helps you choose among them based on the
number of dependent variables (sometimes referred to as outcome variables), the
nature of your independent variables (sometimes referred to as
predictors). You also want to consider the nature of your dependent
variable, namely whether it is an interval variable, ordinal or categorical
variable, and whether it is normally distributed (see What is the difference between categorical, ordinal and interval variables?
for more information on this). The table then shows one or more
statistical tests commonly used given these types of variables (but not
necessarily the only type of test that could be used) and links showing how to
do such tests using SAS, Stata and SPSS.

*Technically, assumptions of normality concern the errors rather than the dependent variable itself. Statistical errors are the deviations of the observed values of the dependent variable from their true or expected values. These errors are unobservable, since we usually do not know the true values, but we can estimate them with residuals, the deviation of the observed values from the model-predicted values. Additionally, many of these models produce estimates that are robust to violation of the assumption of normality, particularly in large samples.

This page was adapted from Choosing the Correct Statistic developed by James D. Leeper, Ph.D.  We thank Professor
Leeper for permission to adapt and distribute this page from our site.